The invention relates generally to an improved method and apparatus for preprocessing a digital image and, more particularly, to preprocessing the digital image by generating the image into a multi-level discrete cosine transform (DCT) pyramid image representation where each level of the pyramid is associated with a different DCT frequency band.
It is known in the art to preprocess a digital image by structuring the image into a pyramid representation where each level of the pyramid is thereafter individually processed as by filtering, sharpening, smoothing and edge detection. Pyramid coding is described, for instance, in a book entitled "Two-Dimensional Signal and Image Processing" by Jae S. Lim, 1990 Prentice-Hall Inc., pp. 632-670, which is herein incorporated by reference to provide supplemental background information which is not essential but is helpful in appreciating the applications of the present invention.
Preprocessing of an image is frequently followed by the reduction or elimination of unwanted artifacts, i.e. noise removal, which in turn is followed by image restoration. Typically, a captured image includes noise inherent in an image signal source, e.g. a camera, scanner, charge-coupled device (CCD), charge-injected device (CID), or the like. Equation (1) mathematically expresses a degraded image or noisy signal x(n.sub.1,n.sub.2) as the addition of noise v(n.sub.1,n.sub.2) to the original signal s(n.sub.1,n.sub.2), as modeled in FIG. 3A. EQU x(n.sub.1,n.sub.2)=s(n.sub.1,n.sub.2)+v(n.sub.1,n.sub.2) (1)
If v(n.sub.1,n.sub.2) is a function of the signal s(n.sub.1, n.sub.2), then x(n.sub.1,n.sub.2) is additive random signal dependent noise (hereinafter "signal dependent noise"), otherwise if v(n.sub.1,n.sub.2) is not dependent upon s(n.sub.1,n.sub.2), then x(n.sub.1,n.sub.2) is additive random signal independent noise (hereinafter "additive noise"). Each of the signals x(n.sub.1,n.sub.2), s(n.sub.1,n.sub.2) and v(n.sub.1,n.sub.2) represents a physical electronic signal, i.e. a waveform of voltages having amplitudes and frequencies related as a function of time.
Examples of signal dependent noise are film grain noise, speckle noise and quantization noise. Signal dependent noise, which is much more difficult to reduce than additive noise, can be reduced by first transforming the noisy signal x(n.sub.1,n.sub.2) into a domain where the noise becomes signal independent, then removing the signal independent noise using a conventional method such as Wiener filtering. Signal dependent noise can also be reduced directly in the spatial domain.
One approach to removing additive noise is disclosed in U.S. Pat. No. 5,337,180 issued 9 Aug. 1994 to Woods et al. which describes optical signal dependent noise reduction by variable spatial thresholding of the fourier transform. Another approach to removing additive noise is disclosed in U.S. Pat. No. 5,327,242 issued 5 Jul. 1994 to Naimpally et al. which describes the reduction of a noise component of a video signal using a three dimensional discrete cosine transform to determine the time frequency spectrum of both the video signal and the noise component, subtracting the time frequency spectrum of the noise component from the time frequency spectrum of the video signal, and converting the time frequency spectrum of the modified video signal back to the spatial domain by using an inverse three dimensional discrete cosine transform. The above two patents are incorporated by reference in their entirety to provide supplemental background information which is not essential but is helpful in appreciating the applications of the present invention.
The book by Jae S. Lim, supra, pp. 527-549, describes additional methods for reducing additive noise, including Wiener filtering and adaptive image processing. Reduction of signal dependent noise is also described on pages 562-567 for processing the noisy signal directly in the signal domain or by first transforming the noisy signal into another domain, then processing for noise reduction. The above pages of the Lim book are herein incorporated by reference for non-essential background information which is helpful in appreciating the applications of the present invention.
It is a primary object of the current invention to provide a system and method for preprocessing a digital image into a multi-level DCT pyramid image representation where each level of the pyramid is associated with a different DCT frequency band and whereby each level of the multi-level DCT pyramid image representation can be further processed, as by filtering, sharpening, smoothing, edge detection, etc.
The above and other objects of the invention will be apparent to those skilled in the art from the following detailed description when read in conjunction with the accompanying drawings and appended claims.